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Ml Inference Jobs in Alabama (NOW HIRING)

IGNITE

AI ENGINEER

Huntsville, AL · On-site

... AI/ML, space and missile defense intelligence, EMSO, advanced analytics, and programmatic domains ... to model hosting, inference pipelines, or cloud-based AI development environments. • ...

Banner Defense, Inc.

AI Engineer

Huntsville, AL · On-site

$130K - $141K/yr

Experience designing, building, integrating, or evaluating AI/ML-enabled applications or software ... Exposure to model hosting, inference pipelines, or cloud-based AI development environments.

Banner Defense, Inc.

AI Engineer

Huntsville, AL · On-site

$130K - $141K/yr

Experience designing, building, integrating, or evaluating AI/ML-enabled applications or software ... Exposure to model hosting, inference pipelines, or cloud-based AI development environments.

Banner Defense, Inc.

AI Engineer

Huntsville, AL · On-site

Experience designing, building, integrating, or evaluating AI/ML-enabled applications or software ... Exposure to model hosting, inference pipelines, or cloud-based AI development environments.

Parsons

AI Engineer

Huntsville, AL · On-site

$134K - $241K/yr

Experience designing, building, integrating, or evaluating AI/ML-enabled applications or software ... Exposure to model hosting, inference pipelines, or cloud-based AI development environments.

Parsons Corporation

AI Engineer

Huntsville, AL · On-site

$134K - $241K/yr

Experience designing, building, integrating, or evaluating AI/ML-enabled applications or software ... Exposure to model hosting, inference pipelines, or cloud-based AI development environments.

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Ml Inference information

What is ML inference?

ML inference refers to the process of using a trained machine learning model to make predictions or decisions based on new data. After a model has been trained on historical data, inference is the phase where that model is deployed and used in real-world applications, such as recognizing speech, detecting objects in images, or recommending products. The focus in ML inference is on speed, efficiency, and scalability to ensure quick predictions, often in real time. This process is critical for practical applications like mobile apps, web services, and embedded systems. Optimizing inference involves reducing latency, memory usage, and computational requirements.

What is the difference between Ml Inference vs Data Scientist?

AspectML InferenceData Scientist
Required CredentialsKnowledge of machine learning models, programming skillsDegree in data science, statistics, or related fields
Work EnvironmentDeploying models in production, real-time data processingData analysis, model development, research
Industry UsageAI product deployment, software companiesResearch institutions, tech firms, consulting

ML Inference focuses on deploying trained models to make predictions on new data, often in real-time. Data Scientists develop and analyze models, working primarily in research and development. While both roles require understanding of machine learning, ML Inference emphasizes deployment and operationalization, whereas Data Scientists focus on model creation and analysis.

What are some common challenges faced by ML inference engineers when deploying models to production?

ML Inference Engineers often encounter challenges such as optimizing model latency and throughput to meet production requirements, ensuring compatibility with diverse hardware environments, and managing model versioning and updates without disrupting service. Additionally, balancing resource utilization and inference accuracy while monitoring real-time performance metrics is crucial. Collaboration with data scientists, DevOps, and software engineers is typically essential to streamline deployment and maintain robust, scalable inference pipelines.

What are the key skills and qualifications needed to thrive in ML inference?

To thrive in ML Inference, you need a solid background in machine learning principles, programming (Python or C++), and experience with deploying models at scale, often supported by a degree in computer science or a related field. Familiarity with frameworks and tools such as TensorFlow, PyTorch, ONNX, and cloud platforms like AWS SageMaker or Google AI Platform is typically required. Strong problem-solving skills, attention to detail, and effective communication are crucial soft skills for collaborating with multidisciplinary teams and optimizing model performance. These skills ensure efficient, scalable, and reliable deployment of machine learning solutions in real-world applications.

Is ML inference a high paying job?

ML inference roles are generally well-paying, especially for those with skills in machine learning frameworks, programming, and cloud platforms. Salaries vary based on experience, location, and industry, but they tend to be higher than average for tech-related positions.
What are popular job titles related to Ml Inference jobs in Alabama? For Ml Inference jobs in Alabama, the most frequently searched job titles are:
What job categories do people searching Ml Inference jobs in Alabama look for? The top searched job categories for Ml Inference jobs in Alabama are:
Ml Inference jobs near you

AI ENGINEER

Ignite Fueling Innovation

Huntsville, AL

Full-time

Re-posted 7 days ago

Job description

Igniters operate in the world's most demanding environment. Igniters are self-motivated, mission-driven, and relentless in solving the Warfighters' hardest problems. We move fast, think differently, and execute with precision to tackle high-stakes challenges across AI/ML, space and missile defense intelligence, EMSO, advanced analytics, and programmatic domains.

As an employee-owned SDVOSB headquartered in Huntsville, AL, our team delivers mission-critical impact for the Army, Air Force, Space Force, MDA, NASA, IC, and FBI. Ignite exists to outpace the threat and deliver results that matter in the moments that count. Ignite is currently seeking a driven, detail-oriented AI Engineer to join our team.

In this role you will get to provide technical support for the design, evaluation, and advancement of AI-enabled applications, tools, and workflows. This role is intended for a hands-on technical professional who understands how modern AI systems are built and can help develop practical solutions using large language models, agentic workflows, orchestration frameworks, and supporting software infrastructure. The right candidate is a builder at heart-someone who explores new frameworks, prototypes ideas, and continuously teaches themselves emerging techniques.

Curiosity and passion matter greatly in this role. We want someone who follows the fast-moving AI ecosystem because they genuinely enjoy it, not just because the job requires itResponsibilities include, but are not limited to: Provide SETA support to government teams developing or assessing AI-enabled applications and tools. Advise on technical approaches for AI application design, prototyping, integration, and evaluation.

Assess solutions involving LLMs, retrieval-augmented generation, tool use, agentic workflows, orchestration layers, prompt pipelines, and evaluation harnesses. Support the review of architectures, development approaches, model usage patterns, and technical risk areas. Advise on AI engineering considerations such as latency, reliability, observability, testability, guardrails, data flow, and system integration.

Evaluate and compare modern AI frameworks, agentic libraries, and experimentation harnesses for suitability in mission use cases. Assist with the development of technical guidance, prototypes, engineering recommendations, and briefings for government stakeholders. Support test and evaluation activities for AI tools, including performance assessment, qualitative review, and workflow validation.

Collaborate with platform engineers, product leads, mission users, and government leadership to ensure AI efforts remain technically grounded and operationally relevant. Stay current on rapid changes in the AI ecosystem and provide informed recommendations on emerging tools and practices. Job Requirements and Qualifications: Required Qualifications Masters degree in Computer Science, Engineering, Data Science, Mathematics, or related field and 12+ years of relevant experience Active Secret clearance required Experience designing, building, integrating, or evaluating AI/ML-enabled applications or software tools.

Familiarity with modern AI application patterns, including LLM-based applications, RAG, prompt engineering, agentic systems, and AI-assisted workflows. Experience with software development fundamentals, APIs, data handling, and system integration. Ability to assess technical tradeoffs and communicate them clearly to both engineers and government stakeholders.

Strong written and verbal communication skills. Desired Qualifications Ability to obtain a TS SCI clearance required Experience with Python, JavaScript/TypeScript, or similar languages commonly used in AI application development. Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses.

Exposure to model hosting, inference pipelines, or cloud-based AI development environments. Understanding of secure AI development practices, data protection, and operational constraints in government settings. Experience supporting defense, cyber, intelligence, or mission application development.

Familiarity with human-in-the-loop workflows, AI assurance, and evaluation methodologies. Working knowledge of one or more agentic or orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, Haystack, LlamaIndex, or similar ecosystems. Security Clearance Requirements: Must have an active Secret Security Clearance Education Requirements: Master's Degree in STEM DisciplineResponsibilities include, but are not limited to: Provide SETA support to government teams developing or assessing AI-enabled applications and tools.

Advise on technical approaches for AI application design, prototyping, integration, and evaluation. Assess solutions involving LLMs, retrieval-augmented generation, tool use, agentic workflows, orchestration layers, prompt pipelines, and evaluation harnesses. Support the review of architectures, development approaches, model usage patterns, and technical risk areas.

Advise on AI engineering considerations such as latency, reliability, observability, testability, guardrails, data flow, and system integration. Evaluate and compare modern AI frameworks, agentic libraries, and experimentation harnesses for suitability in mission use cases. Assist with the development of technical guidance, prototypes, engineering recommendations, and briefings for government stakeholders.

Support test and evaluation activities for AI tools, including performance assessment, qualitative review, and workflow validation. Collaborate with platform engineers, product leads, mission users, and government leadership to ensure AI efforts remain technically grounded and operationally relevant. Stay current on rapid changes in the AI ecosystem and provide informed recommendations on emerging tools and practices.

Job Requirements and Qualifications: Required Qualifications Masters degree in Computer Science, Engineering, Data Science, Mathematics, or related field and 12+ years of relevant experience Active Secret clearance required Experience designing, building, integrating, or evaluating AI/ML-enabled applications or software tools. Familiarity with modern AI application patterns, including LLM-based applications, RAG, prompt engineering, agentic systems, and AI-assisted workflows. Experience with software development fundamentals, APIs, data handling, and system integration.

Ability to assess technical tradeoffs and communicate them clearly to both engineers and government stakeholders. Strong written and verbal communication skills. Desired Qualifications Ability to obtain a TS SCI clearance required Experience with Python, JavaScript/TypeScript, or similar languages commonly used in AI application development.

Familiarity with vector databases, embeddings, model APIs, evaluation frameworks, and agent testing harnesses. Exposure to model hosting, inference pipelines, or cloud-based AI development environments. Understanding of secure AI development practices, data protection, and operational constraints in government settings.

Experience supporting defense, cyber, intelligence, or mission application development. Familiarity with human-in-the-loop workflows, AI assurance, and evaluation methodologies. Working knowledge of one or more agentic or orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, AutoGen, CrewAI, Haystack, LlamaIndex, or similar ecosystems.

Security Clearance Requirements: Must have an active Secret Security Clearance Education Requirements: Master's Degree in STEM Discipline

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